Evidence map›Paper›PMID 29049446›Full record

ArticleJAMA ophthalmology2017

Association of Socioeconomics With Prevalence of Visual Impairment and Blindness.

Wei Wang, William Yan, Andreas Müller, Stuart Keel, Mingguang He

Abstract read
In one paragraph

Article in JAMA ophthalmology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

40 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Wei WangState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, China
William YanCentre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne, East Melbourne, Victoria, Australia
Andreas MüllerCentre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne, East Melbourne, Victoria, Australia
Stuart KeelCentre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne, East Melbourne, Victoria, Australia
Mingguang HeState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, China

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: Vision loss is the third most common impairment worldwide. Although cost-effective interventions are available for preventing or curing most causes of vision loss, availability of these interventions varies considerably between countries and districts. Knowledge of the association between vision loss and socioeconomic factors is informative for public health planning. Objectives: To explore correlations of the prevalence of visual impairment with socioeconomic factors at country levels and to model and estimate a socioeconomic-adjusted disease burden based on these data. Design, Setting, and Participants: In this cross-sectional study, the following data were collected from 190 countries and territories: the age-standardized prevalence of moderate to severe visual impairment (MSVI) and blindness from January 1 to December 31, 2010, across countries, human development index (HDI), gross domestic product (GDP) per capita, total health expenditure, total health expenditure as percentage of GDP (total health expenditure/GDP), public health expenditure as percentage of total health expenditure (public/total health expenditure), and out-of-pocket expenditure as percentage of total health expenditure (out-of-pocket/total health expenditure). Countries were divided into 4 levels (low, medium, high, and very high) by HDI. Data analysis was conducted from September 1, 2016, to July 1, 2017. Main Outcomes and Measures: The correlations between prevalence data and socioeconomic indices were assessed. Results: A strong negative association between prevalence rates of MSVI and blindness and socioeconomic level of development was observed. The mean (SD) age-standardized prevalence of MSVI decreased from 4.38% (1.32%) in low-HDI regions to 1.51% (1.00%) in very-high-HDI regions (P < .001). The national HDI level was attributable to 56.3% of global variation in prevalence rates of MSVI and 67.1% of global variation in prevalence rates of blindness. Higher prevalence rates were also associated with lower total health expenditure per capita, total health expenditure/GDP (β = −0.236 [95% CI, −0.315 to −0.157] for prevalence of MSVI; β = −0.071 [95% CI, −0.100 to −0.042] for prevalence of blindness), public/total health expenditure (β = −0.041 [95% CI, −0.052 to −0.031] for prevalence of MSVI; β = −0.014 [95% CI, −0.018 to −0.010] for prevalence of blindness), and higher percentage of out-of-pocket/total health expenditure (β = 0.044 [95% CI, 0.032-0.055] for prevalence of MSVI; β = 0.013 [95% CI, 0.009-0.017] for prevalence of blindness). Countries with increased burden of visual impairment and blindness can be easily identified by the results of the linear models. Socioeconomic factors could explain 69.4% of the global variations in prevalence of MSVI and 76.3% of the global variations in prevalence of blindness. Conclusions and Relevance: Burden of visual impairment and socioeconomic indicators were closely associated and may help to identify countries requiring greater attention to these issues. The regression modeling described may provide an opportunity to estimate appropriate public health targets that are consistent with a country’s level of socioeconomic development.

Indexed as

Socioeconomic FactorsBlindnessCross-Sectional StudiesDatabases, FactualFemaleGlobal HealthGross Domestic ProductHealth ExpendituresHumansMalePersons with Visual DisabilitiesPrevalenceUnited NationsVision, Low

Identifiers

PMID29049446
PMCPMC6583541

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.